Abstract Extensions to the Visual Odometry Pipeline for the Exploration of Planetary Surfaces
Bibliographic record
Abstract
Mars represents one of the most important targets for space exploration in the next 10 to 30 years, particularly because of evidence of liquid water in the planet’s past. Current environmental conditions dictate that any existing water reserves will be in the form of ice; finding and sampling these ice deposits would further the study of the planet’s climate history, further the search for evidence of life, and facilitate in-situ resource utilization during future manned exploration missions. This thesis presents a suite of algorithms to help enable a robotic ice-prospecting mission to Mars. Starting from visual odometry—the estimation of a rover’s motion using a stereo camera as the primary sensor—we develop the following extensions: (i) a coupled surface/subsurface modelling system that provides novel data products to scientists working remotely, (ii) an autonomous retrotraverse system that allows a rover to return to previously visited places along a route for sampling, or to return a sample to an ascent vehicle, and (iii) the extension of the appearance-based visual odometry pipeline to an actively illuminated light detection and ranging sensor that provides data similar to a stereo camera but is not reliant on consistent ambient lighting, thereby enabling appearance-based vision techniques to be used in environments that are not conducive to passive cameras, such as underground mines or permanently shadowed craters on the moon. All algorithms are evaluated on real data collected using our field robot at the University of Toronto Institute for Aerospace Studies, or at a planetary analogue site on Devon Island, in the
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".